Computer Science > Neural and Evolutionary Computing

Title:
Worm-level Control through Search-based Reinforcement Learning

Abstract: Through natural evolution, nervous systems of organisms formed near-optimal
structures to express behavior. Here, we propose an effective way to create
control agents, by \textit{re-purposing} the function of biological neural
circuit models, to govern similar real world applications. We model the
tap-withdrawal (TW) neural circuit of the nematode, \textit{C. elegans}, a
circuit responsible for the worm's reflexive response to external mechanical
touch stimulations, and learn its synaptic and neural parameters as a policy
for controlling the inverted pendulum problem. For reconfiguration of the
purpose of the TW neural circuit, we manipulate a search-based reinforcement
learning. We show that our neural policy performs as good as existing
traditional control theory and machine learning approaches. A video
demonstration of the performance of our method can be accessed at
\url{this https URL}.